# click the upper-left icon to select videos from the playlist
source: nptelhrd 2013年12月1日
Electronics - Pattern Recognition by Prof. P. S. Sastry, Department of Electronics & Communication Engineering, IISc Bangalore. For more details on NPTEL visit http://nptel.ac.in
28 Feedforward networks for Classification and Regression; Backpropagation in Practice 58:40
13 Linear Discriminant Functions; Perceptron -- Learning Algorithm and convergence proof 58:22
40 Bootstrap, Bagging and Boosting; Classifier Ensembles; AdaBoost 59:31
12 Nonparametric estimation, Parzen Windows, nearest neighbour methods 57:30
39 Assessing Learnt classifiers; Cross Validation; 59:50
27 Backpropagation Algorithm; Representational abilities of feedforward networks 59:01
26 Multilayer Feedforward Neural networks with Sigmoidal activation functions; 58:57
25 Overview of Artificial Neural Networks 59:11
38 No Free Lunch Theorem; Model selection and model estimation; Bias-variance trade-off 59:53
24 VC-Dimension Examples; VC-Dimension of hyperplanes 59:00
11 Convergence of EM algorithm; overview of Nonparametric density estimation 58:18
37 Feature Selection and Dimensionality Reduction; Principal Component Analysis 59:14
23 Complexity of Learning problems and VC-Dimension 58:38
10 Mixture Densities, ML estimation and EM algorithm 57:27
36 Positive Definite Kernels; RKHS; Representer Theorem 58:46
09 Sufficient Statistics; Recursive formulation of ML and Bayesian estimates 58:07
22 Consistency of Empirical Risk Minimization; VC-Dimension 58:14
35 Overview of SMO and other algorithms for SVM; ?-SVM and ?-SVR; SVM as a risk minimizer 58:29
08 Bayesian Estimation examples; the exponential family of densities and ML estimates 57:05
34 Support Vector Regression and ?-insensitive Loss function, examples of SVM learning 58:40
07 Bayesian estimation of parameters of density functions, MAP estimates 57:06
21 Consistency of Empirical Risk Minimization 58:35
20 Overview of Statistical Learning Theory; Empirical Risk Minimization 58:53
19 Learning and Generalization; PAC learning framework 59:02
06 Maximum Likelihood estimation of different densities 58:16
33 Kernel Functions for nonlinear SVMs; Mercer and positive definite Kernels 58:45
05 Implementing Bayes Classifier; Estimation of Class Conditional Densities 58:08
18 Linear Discriminant functions for multi-class case; multi-class logistic regression 57:24
04 Estimating Bayes Error; Minimax and Neymann-Pearson classifiers 57:16
32 SVM formulation with slack variables; nonlinear SVM classifiers 59:00
1. Clicking ▼&► to (un)fold the tree menu may facilitate locating what you want to find. 2. Videos embedded here do not necessarily represent my viewpoints or preferences. 3. This is just one of my several websites. Please click the category-tags below these two lines to go to each independent website.
No comments:
Post a Comment